Collect some Federated Noise Learning papers.
Please give me a ⭐star if you find it useful (❁´◡`❁).
If you find some overlooked papers, please open issues or pull requests(recommended), following the Contributing
section.
Last Update: Dec 27, 2023 16:03:46
- [FedNoRo] FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity (IJCAI) [PDF] [CODE]
- [FedCorr] FedCorr: Multi-Stage Federated Learning for Label Noise Correction (CVPR) [PDF] [CODE]
- [FEDLSR] Towards Federated Learning against Noisy Labels via Local Self-Regularization (CIKM) [PDF] [CODE]
- [SCE] Symmetric Cross Entropy for Robust Learning with Noisy Labels (ICCV) [PDF]
- [GCE] Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels (NeurIPS) [PDF]
- [MAE] Robust Loss Functions under Label Noise for Deep Neural Networks (AAAI) [PDF]
- Unsupervised Label Noise Modeling and Loss Correction (ICML) [PDF]
- [Co-teaching] Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels (NeurIPS) [PDF] [CODE]
You can contribute to this project by opening an issue or creating a pull request on GitHub.
Add paper to the papers.yaml
file with the following format:
- title: "Communication-Efficient Learning of Deep Networks from Decentralized Data"
abbr: FedAvg
year: 2016
conf: AISTAT
links:
PDF: https://arxiv.org/abs/1602.05629.pdf
GitHub:
@misc{awesomeafl,
title = {awesome-asyncrhonous-federated-learning},
author = {miku8miku},
year = {2023},
howpublished = {\\url{https://github.com/beiyuouo/awesome-asynchronous-federated-learning}
}